See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: Maykeye/TinyLLama-v0
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - f23038e56c240d0a_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/f23038e56c240d0a_train_data.json
  type:
    field_input: ''
    field_instruction: query
    field_output: answer
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: fedovtt/8281ac79-b7a0-4889-a9fa-6961426775d3
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_memory:
  0: 76GiB
max_steps: 20
micro_batch_size: 2
mlflow_experiment_name: /tmp/f23038e56c240d0a_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 10
sequence_len: 2048
special_tokens:
  pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 8281ac79-b7a0-4889-a9fa-6961426775d3
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 8281ac79-b7a0-4889-a9fa-6961426775d3
warmup_steps: 10
weight_decay: 0.1
xformers_attention: true

8281ac79-b7a0-4889-a9fa-6961426775d3

This model is a fine-tuned version of Maykeye/TinyLLama-v0 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 8.2830

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0002
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 8
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 10
• training_steps: 20

Training results

Training Loss	Epoch	Step	Validation Loss
8.6163	0.0001	1	8.4795
8.5938	0.0002	2	8.4794
8.7145	0.0003	4	8.4753
8.6569	0.0005	6	8.4589
8.2834	0.0007	8	8.4295
9.0077	0.0009	10	8.3933
8.3223	0.0010	12	8.3530
8.5098	0.0012	14	8.3183
8.3398	0.0014	16	8.2969
8.0738	0.0016	18	8.2854
8.5815	0.0017	20	8.2830

Framework versions

• PEFT 0.13.2
• Transformers 4.46.0
• Pytorch 2.5.0+cu124
• Datasets 3.0.1
• Tokenizers 0.20.1